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Statistical Aspects of a Research Project

This resource explores the importance of statistics in research projects, including descriptive studies, hypothesis testing, experimental design, and analysis. It provides guidance on starting a research project and emphasizes the need for statistical methods.

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Statistical Aspects of a Research Project

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  1. Statistical Aspects of a Research Project Mohd Ridzwan Abd Halim Jabatan Sains Tanaman Universiti Putra Malaysia

  2. Outline • What, why and how • The need for statistics • Two types of study • Decriptive • Hypothesis testing • Treatments, Experimental units and Replications • Experimental Design and Analysis

  3. Starting a Research Project • What? • Why? • How?

  4. WHAT? • What is the objective? • What do you want to find out? • What is the solution to the problem?

  5. WHY? • Why do you want to study that? • Is it new? • Is it a problem? • Is it important? • Can you do it?

  6. WHAT? • Usually your supervisor will tell or guide you • You can also suggest your own

  7. WHY? • You must SEARCH, READ, ASK and obtain information* • FIND OUT what others have done • You must be CONVINCED that it is IMPORTANT to know

  8. HOW? • How can you find the answers? • Experiments? • Treatments? • Statistical Methods?

  9. Why do we need to use Statistical Methods? • Makes results of study valid and acceptable • Helps in deriving conclusions from results • Provides degree of confidence in the conclusion made

  10. What happens if you don’t use statistical methods • Your results will not be accepted • You cannot make a valid conclusion • You cannot answer any question

  11. What you need to do • Determine what you want to find out = OBJECTIVE/S • READ and understand the topic = LITERATURE REVIEW, JUSTIFICATION • Determine what you must do = MATERIALS AND METHODS

  12. MATERIALS & METHODS • How you conduct the study • Two types of study: • Descriptive • Hypothesis testing • Must include the statistical method!

  13. DESCRIPTIVE STUDY • Getting new basic information • e.g. a new crop variety, a survey • No comparisons • No hypothesis • Descriptive statistics – mean, SD, frequency distribution

  14. Descriptive studies • Must have sampling (random, systematic, stratified) • Adequate replications • Representative

  15. Hypothesis testing • Comparing between treatments • Treatments designed to meet objectives • Must have an experimental design

  16. STEP 1 • Determine your treatments: fertilizer? variety? hormone? Method? • Are you studying ONE factor only – SIMPLEST • Are you studying 2 factors – FACTORIAL experiment – more difficult • Are you studying 3 factors – DON’T!!

  17. STEP 2 • Determine your EXPERIMENTAL UNIT = the smallest unit that you apply your treatment • One pot? • One plot? • One plant? • One animal?

  18. STEP 3 • Determine the number of REPLICATIONS = the number of experimental units in one treatment

  19. STEP 4 • Determine the EXPERIMENTAL DESIGN = how you allocate the treatments to the experimental units

  20. CRD vs RCBD • To BLOCK or NOT TO BLOCK?? • If experimental units are HOMOGENEOUS = don’t need blocking = CRD • If experimental units are HETEROGENOUS = need BLOCKING = RCBD

  21. BLOCKING • Group experimental units that are similar • Number of units in one block = number of treatments

  22. RANDOMIZATION • Treatments must be randomized – to avoid bias • You cannot have any influence which treatment goes to which unit

  23. Comparison of padi yields with and without Vita control + Vita Problem = NO REPLICATION

  24. + Vita control Problem = NOT RANDOMIZED + Vita control + Vita control

  25. Replication √ control +vita Randomization √ +vita control control +vita

  26. OK or not? + vita control + Vita Problem – sampling unit treated as exp. unit! No replication!

  27. Replication • Reps are repetition of experimental unit • Sample in an experimental unit are not replications

  28. Four basic elements in experiments • Treatments • Experimental Unit • Replication • Avoiding bias = Randomization

  29. Control 6.3 t +vita 7.8 t Homogeneous units Independent t test +vita 7.9 t Control 7.2 t One-way ANOVA +vita 8.1 t Control 6.9 t Completely Randomized Design (CRD)

  30. t test vs F test (ANOVA) • t test = comparing 2 treatments • F test (ANOVA) = comparing 2 or > 2 treatments

  31. Ladang A Ladang B Ladang C Paired t test 4.0 4.5 Randomized Complete Block Design (RCBD) 5.6 5.9 Two-way ANOVA 3.3 5.2

  32. COMPLETELY RANDOMIZED DESIGN (CRD) ONE-WAY ANOVA 3 treatments 4 reps Homogeneous units

  33. Comparison between treatment means • LSD (least significant difference) =0.12

  34. Program dengan SAS • Data varieti; • Input trt hasil; • Cards; • T1 4.2 • T1 3.9 • Data • ; • Proc anova; • Class trt; • Model hasil=trt; • Means trt/lsd; • run

  35. RANDOMIZED COMPLETE BLOCK DESIGN (RCBD) Blok A Blok B Blok C Blok D

  36. ANOVA RCBD

  37. Program SAS • Proc Anova; • Class trt blok; • Model hasil=trt blok; • Means trt blok/lsd; • Run;

  38. FACTORIAL EXPERIMENTS • Looks at 2 or more factors in one experiment: • Example: • Effects of variety – V1, V2, V3, V3 • Effects of Irrigation – I1, I2, I3 • 4 x 3 factorial • 12 treatment combinations

  39. Treatment Combinations 12 TREATMENTS X 4 REPS = 48 PLOTS

  40. Allocate treatments randomly if CRD

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